Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability--the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs--color (ordered) x shape, color (ordered) x size, size x shape, and size x orientation--in the context of bivariate symbol maps. Both accuracy and speed analyses show that color x shape is the most separable and size x orientation the least separable, while size x color and size x shape do not differ. Separability also proved asymmetric--performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.

翻译：可视化通常通过将属性映射到不同的视觉通道（如颜色、大小或形状）来编码多变量数据。这些编码的有效性取决于可分离性——即通道能被独立感知的程度。然而，尤其在基于地图的语境中，关于可分离性的系统性证据仍然缺乏。我们提出了一项众包实验，用于评估在双变量符号地图背景下四组通道对的可分离性：颜色（有序）×形状、颜色（有序）×大小、大小×形状以及大小×方向。准确性和速度分析均表明，颜色×形状的可分离性最高，大小×方向的可分离性最低，而大小×颜色与大小×形状之间则无显著差异。研究还证明可分离性具有不对称性——表现取决于任务相关变量由哪个通道编码，其中颜色和形状优于大小，而正方形形状尤其难以区分。我们的发现推进了对视觉可分离性的实证理解，对多变量地图设计具有重要启示。